Sharing data between different DBMS types is an inevitable need in Today's diverse IT environments. Need for real-time data integration, seamless migration and data warehousing are the main reasons driving demand for heterogenous replication. In this talk we'll review how open source Tungsten Replicator can replicate data in real-time between databases like MySQL, PostgreSQL, Oracle, MongoDB and others. Join us for this both technical and enlightening talk. We'll cover fundamental steps behind configuring heterogeneous replication, the importance of transaction transforming filters and common challenges rising when replicating cross DBMS-type. We'll conclude with in-line demos to show you how it looks in action.

1. Linas Virbalas
Continuent, Inc.
© Continuent 2010

2. / Definition & Motivation
/ Scoping the Challenge
/ MySQL ->
• PostgreSQL
• Oracle
• MongoDB
/ Demo 1
/ PostgreSQL ->
• MySQL
/ Demo 2
/ Q&A
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4. Heterogeneous Replication

Replication between different types of DBMS
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5. 1. Real-time integration of data between different DBMS
types
2. Seamless migration out of one DBMS type to another
3. Data warehousing (real-time) from different DBMS
types
4. Leveraging specific SQL power of other DBMS types
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6. / Name: Linas Virbalas
/ Country: Lithuania
/ Implementing for Tungsten:
• MySQL -> PostgreSQL
• MySQL -> Greenplum
• MySQL -> Oracle
• PostgreSQL WAL
• PostgreSQL Streaming Replication
• PostgreSQL Logical Replication
via Slony logs
/ Blog:
http://flyingclusters.blogspot.com
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8. 1. MySQL -> …
• Replicating from MySQL to PostgreSQL/Greenplum, Oracle,
MongoDB
2. PostgreSQL -> …
• Replicating from PostgreSQL to MySQL
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9. With Tungsten Replicator
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10. / Open Source GPL v2
/ JAVA
/ Interfaces to implement new:
• Extractors
• Filters
• Appliers
/ Multiple replication services per one process
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11. Technology: Replication Pipelines
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13. / Statement Based Replication
/ Row Based Replication
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15. Master Slave
Replicator Replicator
Transaction Transaction
History Log History Log
Filters Filters
MySQL PostgreSQL
Extractor Applier
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16. / Provisioning
/ Data Type Differences
/ Database vs. Schema
/ Default (Implicitly Defined) Schema Selection
/ SQL Dialect Differences
• Statement Replication vs. Row Replication
/ Character Sets and Binary Data
/ Old Versions of MySQL
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17. Provisioning
/ Harder way: Dump data explicitly
/ Easier way: Replicate a mysqldump backup
Replicator
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18. / Note the type differences between MySQL and PG
MySQL PostgreSQL
! TINYINT SMALLINT
SMALLINT SMALLINT
INTEGER INTEGER
BIGINT BIGINT
! CHAR(1) CHAR(5) = {‘true’, ‘false’}
CHAR(x) CHAR(x)
VARCHAR(x) VARCHAR(x)
DATE DATE
TIMESTAMP TIMESTAMP
! TEXT (diff. sizes) TEXT
! BLOB BYTEA
…
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19. Database vs. Schema
/ In MySQL these are the same:
! !CREATE DATABASE foo!
! !CREATE SCHEMA foo!
/ In PostgreSQL these are very different:
CREATE DATABASE foo!
! !CREATE SCHEMA foo!
/ Tungsten uses filters to rectify MySQL databases to
PostgreSQL schemas
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20. / MySQL: Trivial to use `USE`
/ MySQL: Going without `USE` generates different
events
MySQL Implicit MySQL Explicit
CREATE SCHEMA s; CREATE SCHEMA s;
USE s;
! CREATE TABLE t (i int); CREATE TABLE s.t (i int);
! INSERT INTO t (1); INSERT INTO s.t (1);
/ PG: Extract the default schema from the event
/ PG: Set it before applying
MySQL PostgreSQL
USE s; > SET search_path TO s, "$user”;
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21. / Differences between DDL and DML statement SQL
dialects
/ Row Replication resolves issues rising from
differences in DML, but still leaves DDL to handle
/ Tungsten Replicator Filters come to the rescue!
• Simple to develop Java or JavaScript extensions
• Event structure IN -> Filter -> Event structure OUT
MySQL PostgreSQL
CREATE TABLE complex (id CREATE TABLE complex (id
INTEGER AUTO_INCREMENT SERIAL PRIMARY KEY, i INT);
PRIMARY KEY, i INT);
CREATE TABLE dt (i TINYINT); CREATE TABLE dt (i SMALLINT);
…
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22. / Statement replication: MySQL syntax is “permissive”
/ Embedded binary / alternate charsets
/ Different charsets for different clients
/ Row replication: database/table/column charsets
may differ
/ Answer: Stick with one character set throughout; use
row replication to move binary data
MySQL PostgreSQL
INSERT INTO embedded_blob ARGH!!! (SQL statement fails)
(key, data) VALUES (1, ‘?0^Es
0^0’’)
create table xlate(id int, d1 ARGH!!! (no way to translate to
varchar(25) character set common charset)
latin1, d2 varchar(25)
character set utf8);
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23. MySQL Versions
/ Problem: Data stored on hard-to-replicate MySQL
versions or configurations
• Row replication not enabled (5.1)
• No row replication support (5.0, 4.1)
• Tungsten cannot read binlog (4.1)
/ Answer: MySQL blackhole replication
• (Blackhole = no store, just a binlog)
• Caveat: Check MySQL docs carefully
Replicator
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25. Master Slave
Replicator Replicator
Transaction Transaction
History Log History Log
Filters Filters
MySQL Oracle
Extractor Applier
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26. / TEXT length limitation
• VARCHAR(4000) => CLOB
/ Primary Keys and PrimaryKeyFilter
• Goal:
UPDATE t SET
c1 = x1, c2 = x2, c3 = x3
WHERE
p = p1
• NOT:
UPDATE t SET
c1 = x1, c2 = x2, c3 = x3
WHERE
p = p1 AND c1 = x1 AND c2 = x2 AND c3 = x3 AND …!
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28. > use mydb
switched to db mydb!
> db.test.insert(
{"test": "test value", "anumber" : 5 }
)!
> db.test.find()
{
"_id" : ObjectId("4dce9a4f3d6e186ffccdd4bb"),
"test" : "test value", "anumber" : 5
}!
> exit!
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29. / MySQL binary log doesn’t hold column names
• mysql> INSERT INTO foo (id, data) VALUES
(1, 'hello from MySQL!');
• If nothing done becomes:
> db.foo.find();
{ "_id" : ObjectId("4dc55e45ad90a25b9b57909d"),
" " : "1”,
" " : "hello from MySQL!”}
• Solution: to fill in column names on master side. Then:
> db.foo.find();
{ "_id" : ObjectId("4dc55e45ad90a25b9b57909d"),
” " : "1”,
“ " : "hello from MySQL!”}
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30. MySQL -> MongoDB: The Pipeline
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33. Logical Physical
MySQL Statement Based x
MySQL Row Based x
MySQL Mixed x
PostgreSQL WAL Shipping x
PostgreSQL Streaming Replication x
Filters (data transformation) possible + -
Different data/structure on slave + -
possible
/ A transaction is not accessible to the replicator under
physical replication
/ Tungsten Replicator manages WAL/Streaming
Replication
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34. Logical Physical
MySQL Statement Based x
MySQL Row Based x
MySQL Mixed x
PostgreSQL WAL Shipping x
PostgreSQL Streaming Replication x
Tungsten Replicator w/ x
PostgreSQLSlonyExtractor
Filters (data transformation) possible + -
Different data/structure on slave + -
possible
/ With PostgreSQLSlonyExtractor transaction goes
through the Replicator pipeline
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35. Master Slave
Replicator Replicator
Transaction Transaction
History Log History Log
Filters Filters
PostgreSQL MySQLApplier
SlonyExtractor

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37. / We’ve reviewed an open source heterogeneous
replicator (professional services available upon request)
/ Tungsten Replicator encapsulates the complexity and
corner cases of the subject
/ Replicating:
• out of MySQL – now;
• out of PostgreSQL – prototype;
• out of Oracle – designs ready, awaiting sponsorship.
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39. Open Source Commercial
http://tungsten-replicator.org sales@continuent.com
#tungsten @ irc.freenode.net
My Blog:
http://flyingclusters.blogspot.com
Continuent Web Site:
http://www.continuent.com
© Continuent 2010